d_overall_means = d %>%
group_by(modal, workerid) %>%
summarise(rating_m_overall = mean(rating))
d_indiv_means = d %>%
group_by(modal,percent_middle, workerid) %>%
summarise(rating_m = mean(rating))
d_indiv_merged = merge(d_indiv_means, d_overall_means, by=c("workerid", "modal"))
cors = d_indiv_merged %>%
group_by(workerid) %>%
summarise(corr = cor(rating_m, rating_m_overall))
exclude = cors %>%
filter(corr > 0.75) %>%
.$workerid
print(paste("Excluded", length(exclude), "participants based on random responses."))
## [1] "Excluded 13 participants based on random responses."
d = d %>% filter(!(workerid %in% exclude))
## Individual plots
plot(ps1$by_participant)
##
## Two Sample t-test
##
## data: aucs.positive$auc_diff and aucs.negative$auc_diff
## t = 0.24409, df = 132, p-value = 0.8075
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.824517 8.745797
## sample estimates:
## mean of x mean of y
## -0.01512868 -0.97576872